Lesson 4
Scatterplots and Perceived Audience Size
Notes:
Scatterplots
Notes:
setwd("/Users/brianchase/R")
getwd()## [1] "/Users/brianchase/R"library(ggplot2)
pf <- read.csv('pseudo_facebook.tsv', sep = '\t')
qplot(x = age, y = friend_count, data = pf)
qplot(age, friend_count, data = pf)
What are some things that you notice right away?
Response:
ggplot Syntax
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) + geom_point() +
xlim(13, 90)## Warning: Removed 4906 rows containing missing values (geom_point).
Overplotting
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_point(alpha = 1/20) +
xlim(13, 90)## Warning: Removed 4906 rows containing missing values (geom_point).
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_jitter(alpha = 1/20) +
xlim(13, 90)## Warning: Removed 5184 rows containing missing values (geom_point).
What do you notice in the plot?
Response:
Coord_trans()
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) +
geom_point(alpha = 1/20, position = position_jitter(h = 0)) +
xlim(13, 90) +
coord_trans(y = "sqrt")## Warning: Removed 5184 rows containing missing values (geom_point).
Look up the documentation for coord_trans() and add a layer to the plot that transforms friend_count using the square root function. Create your plot!
What do you notice?
Alpha and Jitter
Notes:
ggplot(aes(x = age, y = friendships_initiated), data = pf) +
geom_point(alpha = 1/30, position = position_jitter(h = 0)) +
coord_trans(y = "sqrt")
Overplotting and Domain Knowledge
Notes:
Conditional Means
Notes:
#install.packages('dplyr')
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionage_groups <- group_by(pf, age)
pf.fc_by_age <- summarise(age_groups,
friend_count_mean = mean(friend_count),
friend_count_median = median(friend_count),
n = n())
pf.fc_by_age <-arrange(pf.fc_by_age, age)
head(pf.fc_by_age)## Source: local data frame [6 x 4]
##
## age friend_count_mean friend_count_median n
## (int) (dbl) (dbl) (int)
## 1 13 164.7500 74.0 484
## 2 14 251.3901 132.0 1925
## 3 15 347.6921 161.0 2618
## 4 16 351.9371 171.5 3086
## 5 17 350.3006 156.0 3283
## 6 18 331.1663 162.0 5196Conditional Means Alternate Code
#install.packages('dplyr')
library(dplyr)
pf.fc_by_age <- pf %>%
group_by(age) %>%
summarise(friend_count_mean = mean(friend_count),
friend_count_median = median(friend_count),
n = n()) %>%
arrange(age)
head(pf.fc_by_age, 20)## Source: local data frame [20 x 4]
##
## age friend_count_mean friend_count_median n
## (int) (dbl) (dbl) (int)
## 1 13 164.7500 74.0 484
## 2 14 251.3901 132.0 1925
## 3 15 347.6921 161.0 2618
## 4 16 351.9371 171.5 3086
## 5 17 350.3006 156.0 3283
## 6 18 331.1663 162.0 5196
## 7 19 333.6921 157.0 4391
## 8 20 283.4991 135.0 3769
## 9 21 235.9412 121.0 3671
## 10 22 211.3948 106.0 3032
## 11 23 202.8426 93.0 4404
## 12 24 185.7121 92.0 2827
## 13 25 131.0211 62.0 3641
## 14 26 144.0082 75.0 2815
## 15 27 134.1473 72.0 2240
## 16 28 125.8354 66.0 2364
## 17 29 120.8182 66.0 1936
## 18 30 115.2080 67.5 1716
## 19 31 118.4599 63.0 1694
## 20 32 114.2800 63.0 1443Create your plot!
ggplot(aes(x = age, y = friend_count_median), data = pf.fc_by_age) +
geom_line() +
scale_x_continuous(breaks = seq(13, 113, 2), limits = c(13, 113)) +
coord_trans(y = "sqrt")
summary(pf.fc_by_age)## age friend_count_mean friend_count_median n
## Min. : 13 Min. : 84.02 Min. : 45.0 Min. : 9.0
## 1st Qu.: 38 1st Qu.:108.15 1st Qu.: 54.5 1st Qu.: 136.0
## Median : 63 Median :134.15 Median : 66.0 Median : 801.0
## Mean : 63 Mean :212.46 Mean :106.8 Mean : 980.2
## 3rd Qu.: 88 3rd Qu.:333.85 3rd Qu.:162.0 3rd Qu.:1099.0
## Max. :113 Max. :484.94 Max. :275.0 Max. :5196.0Overlaying Summaries with Raw Data
Notes:
ggplot(aes(x = age, y = friend_count), data = pf) +
xlim(13,90) +
geom_point(alpha = 0.05,
position = position_jitter(h = 0),
color = 'orange') +
coord_trans(y= 'sqrt') +
geom_line(stat = 'summary', fun.y = mean) +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .1),
linetype = 2, color = 'blue') +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .5),
color = 'blue') +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .9),
linetype = 2, color = 'blue') ## Warning: Removed 4906 rows containing non-finite values (stat_summary).
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
## Warning: Removed 4906 rows containing non-finite values (stat_summary).
## Warning: Removed 4906 rows containing non-finite values (stat_summary).## Warning: Removed 5172 rows containing missing values (geom_point).
Zoom into data with coord_cartesian function
ggplot(aes(x = age, y = friend_count), data = pf) +
coord_cartesian(xlim = c(13,70), ylim = c(0,1000)) +
geom_point(alpha = 0.05,
position = position_jitter(h = 0),
color = 'orange') +
geom_line(stat = 'summary', fun.y = mean) +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .1),
linetype = 2, color = 'blue') +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .5),
color = 'blue') +
geom_line(stat = 'summary', fun.y = quantile, fun.args = list(probs = .9),
linetype = 2, color = 'blue') 
What are some of your observations of the plot?
Response:
Moira: Histogram Summary and Scatterplot
See the Instructor Notes of this video to download Moira’s paper on perceived audience size and to see the final plot.
Notes:
Correlation
Notes:
cor.test(pf$age, pf$friend_count, method = "pearson")##
## Pearson's product-moment correlation
##
## data: pf$age and pf$friend_count
## t = -8.6268, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03363072 -0.02118189
## sample estimates:
## cor
## -0.02740737with(pf, cor.test(age, friend_count, method = 'pearson'))##
## Pearson's product-moment correlation
##
## data: age and friend_count
## t = -8.6268, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03363072 -0.02118189
## sample estimates:
## cor
## -0.02740737Look up the documentation for the cor.test function.
What’s the correlation between age and friend count? Round to three decimal places. Response:
Correlation on Subsets
Notes:
with(subset(pf, age <= 70), cor.test(age, friend_count))##
## Pearson's product-moment correlation
##
## data: age and friend_count
## t = -52.592, df = 91029, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1780220 -0.1654129
## sample estimates:
## cor
## -0.1717245correlation pearson kendall spearman
Correlation Methods
Notes:
Create Scatterplots
Notes:
ggplot(aes(x = www_likes_received, y = likes_received), data = pf) +
geom_point() +
xlim(0, quantile(pf$www_likes_recieved, 0.95)) +
ylim(0, quantile(pf$likes_recieved, 0.95)) +
geom_smooth(method = 'lm', color = 'red')
Strong Correlations
Notes:
ggplot(aes(x = www_likes_received, y = likes_received), data = pf) +
geom_point() +
xlim(0, quantile(pf$www_likes_received, 0.95)) +
ylim(0, quantile(pf$likes_received, 0.95)) +
geom_smooth(method = 'lm', color = 'red')## Warning: Removed 6075 rows containing non-finite values (stat_smooth).## Warning: Removed 6075 rows containing missing values (geom_point).
What’s the correlation betwen the two variables? Include the top 5% of values for the variable in the calculation and round to 3 decimal places.
with(pf, cor.test(www_likes_received, likes_received, method = 'pearson'))##
## Pearson's product-moment correlation
##
## data: www_likes_received and likes_received
## t = 937.1, df = 99001, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.9473553 0.9486176
## sample estimates:
## cor
## 0.9479902Response:
Moira on Correlation
Notes:
More Caution with Correlation
Notes:
#install.packages('alr3')
library(alr3)## Loading required package: car## Warning: package 'car' was built under R version 3.2.4data(Mitchell)
?Mitchell
ggplot(aes(x = Month, y = Temp), data = Mitchell) +
geom_point()
Create your plot!
Noisy Scatterplots
Take a guess for the correlation coefficient for the scatterplot.
What is the actual correlation of the two variables? (Round to the thousandths place)
with(Mitchell, cor.test(Month, Temp, method = 'pearson'))##
## Pearson's product-moment correlation
##
## data: Month and Temp
## t = 0.81816, df = 202, p-value = 0.4142
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08053637 0.19331562
## sample estimates:
## cor
## 0.05747063Making Sense of Data
Notes:
summary(Mitchell$Month)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00 50.75 101.50 101.50 152.20 203.00ggplot(data = Mitchell, aes(x = Month, y = Temp)) +
geom_point() +
scale_x_discrete(breaks =seq(0,203,12))
ggplot(aes(x=(Month%%12),y=Temp),data=Mitchell)+
geom_point()
A New Perspective
What do you notice? Response:
Watch the solution video and check out the Instructor Notes! Notes:
Understanding Noise: Age to Age Months
Notes:
ggplot(aes(x = age, y = friend_count_mean), data = pf.fc_by_age) +
geom_line()
head(pf.fc_by_age,10)## Source: local data frame [10 x 4]
##
## age friend_count_mean friend_count_median n
## (int) (dbl) (dbl) (int)
## 1 13 164.7500 74.0 484
## 2 14 251.3901 132.0 1925
## 3 15 347.6921 161.0 2618
## 4 16 351.9371 171.5 3086
## 5 17 350.3006 156.0 3283
## 6 18 331.1663 162.0 5196
## 7 19 333.6921 157.0 4391
## 8 20 283.4991 135.0 3769
## 9 21 235.9412 121.0 3671
## 10 22 211.3948 106.0 3032pf.fc_by_age[17:19, ]## Source: local data frame [3 x 4]
##
## age friend_count_mean friend_count_median n
## (int) (dbl) (dbl) (int)
## 1 29 120.8182 66.0 1936
## 2 30 115.2080 67.5 1716
## 3 31 118.4599 63.0 1694Create an age_with_months variable
pf$age_with_months <- with(pf, age + (1 - dob_month / 12))Age with Months Means
Programming Assignment
pf.fc_by_age_months <- pf %>%
group_by(age_with_months) %>%
summarise(friend_count_mean = mean(friend_count),
friend_count_median = median(friend_count),
n = n()) %>%
arrange(age_with_months)
head(pf.fc_by_age_months, 10)## Source: local data frame [10 x 4]
##
## age_with_months friend_count_mean friend_count_median n
## (dbl) (dbl) (dbl) (int)
## 1 13.16667 46.33333 30.5 6
## 2 13.25000 115.07143 23.5 14
## 3 13.33333 136.20000 44.0 25
## 4 13.41667 164.24242 72.0 33
## 5 13.50000 131.17778 66.0 45
## 6 13.58333 156.81481 64.0 54
## 7 13.66667 130.06522 75.5 46
## 8 13.75000 205.82609 122.0 69
## 9 13.83333 215.67742 111.0 62
## 10 13.91667 162.28462 71.0 130Noise in Conditional Means
ggplot(aes(x = age_with_months, y = friend_count_mean),
data = subset(pf.fc_by_age_months, age_with_months < 71)) +
geom_line()
Smoothing Conditional Means
Notes:
p1 <- ggplot(aes(x = age, y = friend_count_mean),
data = subset(pf.fc_by_age, age < 71)) +
geom_line() +
geom_smooth()
p2 <- ggplot(aes(x = age_with_months, y = friend_count_mean),
data = subset(pf.fc_by_age_months, age_with_months < 71)) +
geom_line() +
geom_smooth()
p3 <- ggplot(aes(x = round(age/5) * 5, y = friend_count),
data = subset(pf, age < 71)) +
geom_line(stat = 'summary', fun.y = mean)
library(gridExtra)
grid.arrange(p2,p1,p3, ncol = 1)
Which Plot to Choose?
Notes:
Analyzing Two Variables
Reflection:
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